{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "异常值检测（outlier）是一种数据挖掘过程，用于确定数据集中发现的异常值并确定其出现的详细信息。当前自动异常检测至关重要，因为大量数据无法手动标记异常值。 自动异常检测具有广泛的应用，例如信用卡欺诈检测，系统健康监测，故障检测以及传感器网络中的事件检测系统等。今天我们就通过使用python来实现异常值的自动检测系统的实战开发。我们将会使用以下技术来实现异常值检测:\n",
    "\n",
    "* KMeans\n",
    "* PCA\n",
    "* IsolationForest\n",
    "* SVM\n",
    "* EllipticEnvelope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.dates as md\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.covariance import EllipticEnvelope\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.svm import OneClassSVM\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  \n",
    "plt.rcParams['axes.unicode_minus'] = False \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据\n",
    "\n",
    "我们的数据kaggle你可以在这里<a href=\"https://www.kaggle.com/c/expedia-personalized-sort/data\">下载</a>。Expedia是全球最大的在线旅行社（OTA,类似我们的携程网），它每天为数百万旅行购物者提供搜索服务其中包括用户在Expedia网站上搜索酒店的相关信息,如国家，地区，房型，价格，入住天数，入住时间等信息。  \n",
    "\n",
    "我们想通过这个数据集来检测其中价格的异常值。由于Expedia提供的数据集非常大,为了能很好的演示我们的异常值检测功能，我们将从Expedia数据集中过滤出一个子集，该子集只包含用户查询的酒店标间(srch_room_count=1)和酒店所在地为美国(visitor_location_country_id=219)的信息。字段的含义如下:\n",
    "\n",
    "* prop_id：酒店Id\n",
    "* datetime: 用户查询的时间\n",
    "* price_usd：价格(美元)\n",
    "* srch_booking_window:从查询日期开始的酒店住宿天数\n",
    "* srch_saturday_night_bool：如果住宿从周四晚上开始，小于等于4个晚上(必须包含周六晚上)则为1，否则为0  \n",
    "\n",
    " 我们会看到同一家酒店,不同的住宿天数，是否包含周六晚，都会导致标间(单间)价格的不同,我们将从中找出价格的异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>price_usd</th>\n",
       "      <th>srch_booking_window</th>\n",
       "      <th>srch_saturday_night_bool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-14 11:27:28</td>\n",
       "      <td>206.0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-03 20:48:24</td>\n",
       "      <td>186.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-01-19 16:51:27</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013-01-26 11:34:23</td>\n",
       "      <td>72.0</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-04-29 09:39:50</td>\n",
       "      <td>246.0</td>\n",
       "      <td>245</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013-03-25 19:43:44</td>\n",
       "      <td>52.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013-05-19 19:52:42</td>\n",
       "      <td>68.0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2012-11-14 17:18:51</td>\n",
       "      <td>46.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013-01-10 19:22:49</td>\n",
       "      <td>50.0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013-03-27 14:49:27</td>\n",
       "      <td>122.0</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              date_time  price_usd  srch_booking_window  \\\n",
       "2   2013-03-14 11:27:28      206.0                   99   \n",
       "4   2013-01-03 20:48:24      186.0                    6   \n",
       "5   2013-01-19 16:51:27       61.0                    1   \n",
       "6   2013-01-26 11:34:23       72.0                  116   \n",
       "7   2013-04-29 09:39:50      246.0                  245   \n",
       "8   2013-03-25 19:43:44       52.0                    7   \n",
       "12  2013-05-19 19:52:42       68.0                   42   \n",
       "14  2012-11-14 17:18:51       46.0                    4   \n",
       "15  2013-01-10 19:22:49       50.0                   46   \n",
       "16  2013-03-27 14:49:27      122.0                  100   \n",
       "\n",
       "    srch_saturday_night_bool  \n",
       "2                          1  \n",
       "4                          0  \n",
       "5                          0  \n",
       "6                          0  \n",
       "7                          0  \n",
       "8                          0  \n",
       "12                         0  \n",
       "14                         0  \n",
       "15                         0  \n",
       "16                         1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df = pd.read_csv('./data/expedia_train.csv')\n",
    "df = pd.read_csv('./data/expedia_train1.csv')\n",
    "#过滤Id为104517的酒店\n",
    "df = df.loc[df['prop_id'] == 104517]\n",
    "#过滤标间\n",
    "df = df.loc[df['srch_room_count'] == 1]\n",
    "#219表示美国\n",
    "df = df.loc[df['visitor_location_country_id'] == 219]\n",
    "df = df[['date_time', 'price_usd', 'srch_booking_window', 'srch_saturday_night_bool']]\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们看一下数据集元数据信息:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3049 entries, 2 to 4732\n",
      "Data columns (total 4 columns):\n",
      "date_time                   3049 non-null object\n",
      "price_usd                   3049 non-null float64\n",
      "srch_booking_window         3049 non-null int64\n",
      "srch_saturday_night_bool    3049 non-null int64\n",
      "dtypes: float64(1), int64(2), object(1)\n",
      "memory usage: 119.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们发现变量date_time的类型不是datetime类型，这会使我们在做可视化的时候出现问题，所以我们要将date_tiem的类型设置为datetime型，接下来我们主要目的是发现价格(price_usd)的异常值,所以我们首先看一下价格的分布情况:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    3049.000000\n",
      "mean      112.939023\n",
      "std       113.374049\n",
      "min         0.120000\n",
      "25%        67.000000\n",
      "50%       100.000000\n",
      "75%       141.000000\n",
      "max      5584.000000\n",
      "Name: price_usd, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb7f9780>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#将date_time的类型设置为datetime\n",
    "df['date_time'] = pd.to_datetime(df['date_time'])\n",
    "df = df.sort_values('date_time')\n",
    "print(df['price_usd'].describe())\n",
    "df['price_usd'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们发现价格的均值是112，但是最大值却是5584. 这个一个极端的最大值。似乎所有价格数据都小于500，只有一个极端最大值5584。为了我们在后面能找到更多不是极端的异常值，我们先删除这个极端最大值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    3048.000000\n",
      "mean      111.144055\n",
      "std        55.055161\n",
      "min         0.120000\n",
      "25%        67.000000\n",
      "50%       100.000000\n",
      "75%       141.000000\n",
      "max       536.000000\n",
      "Name: price_usd, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb8b1320>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oWn4I2A38ywaOJ7VOU9/0Wddqb0AP4k3pjRGZuTE7jjgIfDIzn4iI64DLMvPAonWmgenq5U8B/1xzuPOB79c+2NFgj+3Q9h7b3h9svh4vzswLlltpI2f2C8BYtTzOgEtGmTkDzKx1oIh4PDMn17qfzcwe26HtPba9PxjdHjfyBu0c/Us3ALuA+Q0cS5L0MjZyZv8F4EhEXAhcD1yxgWNJkl7Ghs3sM/ME/Zu0jwFTmXl8o8ZiHS4FjQB7bIe299j2/mBEe9ywG7SSpM3DJ2glqQCGvbTOIuJVEbEnIs5v+likU0Y+7Nv2lQwR0YmII9Xy1oi4PyIeiYjblqqNkojYHhFfjoiHIuLzEXHuoHM4quc1InYAfw28GfhaRFzQpv7OVP1Z/Xa13KoeI+KciPjPiOhVvyYi4rcj4lsR8YdnrHdWbbMa6bBv21cyVEFxH7CtKt0BzGXmVcBNEXHeErVRshf4RGZeBzwN3MKiczji5/UNwAcy8yPAg8A1tKu/M30cGBvUTwt6fAPw2czsZmYXOJf+R8nfDHwvIq6NiMsX1xo72hUY6bBn8FcyjLIXgZuBE9XrLqf7exiYXKI2MjLzU5n51erlBcB7OPscdgfURkJmfj0zH4uIt9IPgXfQov5OiYhrgOfp/4PdpX09XgH8XER8s/o2gLcDf5X9T7Q8CFwNvG1AbdMa9bDfBhyrlp8FOg0ey5pl5olFH1Ed1F8reo6IK4EdwH/Rsh4jIuj/o/0ckLSvv3OB3wT2V6U2/jn9FnBtZr4Z2Er/2wBGusdRD/tlv5JhxA3qb+R7johXAfcAt9HCHrPvduA7wFtoWX/0Q/5TmfmD6nXrziHwncz872r5cVrQ46Y+uBVo+1cyDOpvpHuuZoWfA+7MzCdpWY8R8cGIeF/18pXAAVrUX+Va4PaI6AFvBN5J+3r8s4jYFRFbgHfTn8WPdI8j/VBVRLwCOAL8LdVXMmzwk7pDERG9zOxGxMXAYeBv6M8QrwBevbiWmS82drCrFBHvB34XeKIq3Qt8gDPOIf1LHyN5Xqub7IeAHwX+AbiT/r2VVvS3WBX472JRP4x4jxHxeuAvgAC+RP+y1RH6s/yfrX49ubiWmf/RyAGvwEiHPfzwL9ce4OHMfLrp41lv1XcL7QYePPWXZVBtlA06h206r23vD4rpcQy4Afj7zPz3pWqb1ciHvSRpeaN+zV6StAKGvSQVwLCXpAIY9pJUAMNekgrw/8ZTMeHQOwiHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.loc[df['price_usd'] < 5584]\n",
    "print(df['price_usd'].describe())\n",
    "df['price_usd'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "删除价格的极端最大值以后,价格分布基本趋于正常(略微右偏)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 时间序列可视化\n",
    "\n",
    "下面我们根据时间对价格进行可视化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '价格(美元)')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='date_time', y='price_usd', figsize=(12,6))\n",
    "plt.xlabel('日期')\n",
    "plt.ylabel('价格(美元)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>price_usd</th>\n",
       "      <th>srch_booking_window</th>\n",
       "      <th>srch_saturday_night_bool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>2012-11-01 02:48:30</td>\n",
       "      <td>84.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2012-11-01 03:06:43</td>\n",
       "      <td>78.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624</th>\n",
       "      <td>2012-11-01 09:04:18</td>\n",
       "      <td>114.0</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2486</th>\n",
       "      <td>2012-11-01 09:11:03</td>\n",
       "      <td>76.0</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3375</th>\n",
       "      <td>2012-11-01 10:15:25</td>\n",
       "      <td>128.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               date_time  price_usd  srch_booking_window  \\\n",
       "1905 2012-11-01 02:48:30       84.0                   19   \n",
       "32   2012-11-01 03:06:43       78.0                   16   \n",
       "1624 2012-11-01 09:04:18      114.0                   56   \n",
       "2486 2012-11-01 09:11:03       76.0                   56   \n",
       "3375 2012-11-01 10:15:25      128.0                    0   \n",
       "\n",
       "      srch_saturday_night_bool  \n",
       "1905                         0  \n",
       "32                           1  \n",
       "1624                         1  \n",
       "2486                         1  \n",
       "3375                         1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df.loc[df['srch_saturday_night_bool'] == 0, 'price_usd']\n",
    "b = df.loc[df['srch_saturday_night_bool'] == 1, 'price_usd']\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(a, bins = 50, alpha=0.5, label='不含周六晚上')\n",
    "plt.hist(b, bins = 50, alpha=0.5, label='含周六晚上')\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel('价格')\n",
    "plt.ylabel('数量')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面的直方图可知含周六晚上的(srch_saturday_night_bool=1)的价格均值要大于不含周六晚上的(srch_saturday_night_bool=1)价格均值。含周末的房价略高一些，这应该是合理的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1599\n",
       "0    1449\n",
       "Name: srch_saturday_night_bool, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['srch_saturday_night_bool'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 基于聚类的异常检测\n",
    "\n",
    "k-means是一种广泛使用的聚类算法。 它创建了k个具有相似特性的数据组。 不属于这些组的数据实例可能会被标记为异常。 在我们开始k-means聚类之前，我们使用elbow方法来确定最佳聚类数量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[['price_usd', 'srch_booking_window', 'srch_saturday_night_bool']]\n",
    "n_cluster = range(1, 20)\n",
    "kmeans = [KMeans(n_clusters=i).fit(data) for i in n_cluster]\n",
    "scores = [kmeans[i].score(data) for i in range(len(kmeans))]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.plot(n_cluster, scores)\n",
    "plt.xlabel('聚类集群数')\n",
    "plt.ylabel('分数')\n",
    "plt.title('Elbow 曲线')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了找出合理的距离中心数，我们尝试尽可能多的聚类中心数（从1个到20个聚类中心），然后我们画出Elbow曲线，通过观察Elbow曲线,我们发现当我们的聚类中心数量增加到10个以上时Elbow曲线趋于收敛，因此我们大致可以将聚类中心数定为10.\n",
    "\n",
    "下面我们将K-means算法的n_clusters设置为10,然后我们将数据进行3D可视化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df[['price_usd', 'srch_booking_window', 'srch_saturday_night_bool']]\n",
    "X = X.reset_index(drop=True)\n",
    "km = KMeans(n_clusters=10)\n",
    "km.fit(X)\n",
    "km.predict(X)\n",
    "labels = km.labels_\n",
    "\n",
    "fig = plt.figure(1, figsize=(7,7))\n",
    "ax = Axes3D(fig, rect=[0, 0, 0.95, 1], elev=48, azim=134)\n",
    "ax.scatter(X.iloc[:,0], X.iloc[:,1], X.iloc[:,2],\n",
    "          c=labels.astype(np.float), edgecolor=\"k\")\n",
    "ax.set_xlabel(\"price_usd\")\n",
    "ax.set_ylabel(\"srch_booking_window\")\n",
    "ax.set_zlabel(\"srch_saturday_night_bool\")\n",
    "plt.title(\"K Means\", fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来我们要确定需要保留数据中的哪些主要成分(特征)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[['price_usd', 'srch_booking_window', 'srch_saturday_night_bool']]\n",
    "X = data.values\n",
    "\n",
    "#标准化处理,均值为0,标准差为1\n",
    "X_std = StandardScaler().fit_transform(X)\n",
    "mean_vec = np.mean(X_std, axis=0)\n",
    "#计算协方差\n",
    "cov_mat = np.cov(X_std.T)\n",
    "\n",
    "#计算特征值和特征向量\n",
    "eig_vals, eig_vecs = np.linalg.eig(cov_mat)\n",
    "\n",
    "#每个特征值对应一组特征向量\n",
    "eig_pairs = [ (np.abs(eig_vals[i]),eig_vecs[:,i]) for i in range(len(eig_vals))]\n",
    "eig_pairs.sort(key = lambda x: x[0], reverse= True)\n",
    "\n",
    "#特征值求和\n",
    "tot = sum(eig_vals)\n",
    "\n",
    "#每个主要成分的解释方差\n",
    "var_exp = [(i/tot)*100 for i in sorted(eig_vals, reverse=True)] \n",
    "#累计的解释方差\n",
    "cum_var_exp = np.cumsum(var_exp) \n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(range(len(var_exp)), var_exp, alpha=0.3, align='center', label='独立的解释方差', color = 'g')\n",
    "plt.step(range(len(cum_var_exp)), cum_var_exp, where='mid',label='累积解释方差')\n",
    "plt.ylabel('解释方差率')\n",
    "plt.xlabel('主成分')\n",
    "plt.legend(loc='best')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们首先对数据进行标准化处理(StandardScaler)，然后再计算特征变量之间的协方差矩阵,协方差矩阵反应了特征变量之间的相关性，如果两个特征变量之间的协方差为正则说明它们之间是正相关关系,如果为负则说明它们之间是负相关关系，如果为0则说明特征变量之间是相互独立的关系,不存在相关关系(有时候我们也会用相关系数矩阵来代替协方差矩阵)。然后在协方差矩阵的基础上又计算了协方差矩阵的特征值和特征向量，根据特征值计算出每个主成分(特征)的解释方差,以及累计解释方差，我们这样做的目的是为了下一步做主成分分析(PCA)挑选出特征变量中的主成分。我们挑选前2个主成分，因为它们的累计解释方差为80%。\n",
    "\n",
    "从上图可知我们的三个主成分,第一个主成分(特征)解释了将近50%的方差变化,第二个主成分解释了近30%的方差变化,那么前2个主成分解释了近80%的方差。因此接下来我们将使用PCA算法进行降维并将设置参数n_components=2。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df[['price_usd', 'srch_booking_window', 'srch_saturday_night_bool']]\n",
    "X = data.values\n",
    "\n",
    "#标准化处理,均值为0,标准差为1\n",
    "X_std = StandardScaler().fit_transform(X)\n",
    "data = pd.DataFrame(X_std)\n",
    "\n",
    "#将特征维度降到2\n",
    "pca = PCA(n_components=2)\n",
    "data = pca.fit_transform(data)\n",
    "# 降维后将2个新特征进行标准化处理\n",
    "scaler = StandardScaler()\n",
    "np_scaled = scaler.fit_transform(data)\n",
    "data = pd.DataFrame(np_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #calculate with different number of centroids to see the loss plot (elbow method)\n",
    "# n_cluster = range(1, 20)\n",
    "# kmeans = [KMeans(n_clusters=i).fit(data) for i in n_cluster]\n",
    "# scores = [kmeans[i].score(data) for i in range(len(kmeans))]\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(10,6))\n",
    "# ax.plot(n_cluster, scores)\n",
    "# plt.xlabel('Number of Clusters')\n",
    "# plt.ylabel('Score')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>price_usd</th>\n",
       "      <th>srch_booking_window</th>\n",
       "      <th>srch_saturday_night_bool</th>\n",
       "      <th>cluster</th>\n",
       "      <th>principal_feature1</th>\n",
       "      <th>principal_feature2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-11-01 02:48:30</td>\n",
       "      <td>84.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.889864</td>\n",
       "      <td>-0.521900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-11-01 03:06:43</td>\n",
       "      <td>78.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.230566</td>\n",
       "      <td>-0.272218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-11-01 09:04:18</td>\n",
       "      <td>114.0</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.567439</td>\n",
       "      <td>0.546642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-11-01 09:11:03</td>\n",
       "      <td>76.0</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.155659</td>\n",
       "      <td>0.581776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-11-01 10:15:25</td>\n",
       "      <td>128.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.793675</td>\n",
       "      <td>-0.659305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date_time  price_usd  srch_booking_window  \\\n",
       "0 2012-11-01 02:48:30       84.0                   19   \n",
       "1 2012-11-01 03:06:43       78.0                   16   \n",
       "2 2012-11-01 09:04:18      114.0                   56   \n",
       "3 2012-11-01 09:11:03       76.0                   56   \n",
       "4 2012-11-01 10:15:25      128.0                    0   \n",
       "\n",
       "   srch_saturday_night_bool  cluster  principal_feature1  principal_feature2  \n",
       "0                         0        1           -0.889864           -0.521900  \n",
       "1                         1        5            0.230566           -0.272218  \n",
       "2                         1        2            0.567439            0.546642  \n",
       "3                         1        2            0.155659            0.581776  \n",
       "4                         1        9            0.793675           -0.659305  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = [KMeans(n_clusters=i).fit(data) for i in n_cluster]\n",
    "df['cluster'] = kmeans[9].predict(data)\n",
    "df.index = data.index\n",
    "df['principal_feature1'] = data[0]\n",
    "df['principal_feature2'] = data[1]\n",
    "# df['cluster'].value_counts()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基于聚类的异常检测中的假设是，如果我们对数据进行聚类，则正常数据将属于聚类，而异常将不属于任何聚类或属于小聚类。 我们使用以下步骤来查找和可视化异常值。\n",
    "\n",
    "* 计算每个数据点与其最近的聚类中心之间的距离。 最大的距离被认为是异常的。\n",
    "* 设置一个异常值的比例outliers_fraction为1%,这样设置是因为在标准正太分布的情况下（N(0,1)）我们一般认定3个标准差以外的数据为异常值,3个标准差以内的数据包含了数据集中99%以上的数据,所以剩下的1%的数据可以视为异常值。\n",
    "* 根据异常值比例outliers_fraction，计算异常值的数量number_of_outliers\n",
    "* 设置一个判定异常值的阈值threshold\n",
    "* 通过阈值threshold来判定数据是否为异常值\n",
    "* 对数据进行可视化(包含正常数据和异常数据)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "# 计算每个数据点到其聚类中心的距离\n",
    "def getDistanceByPoint(data, model):\n",
    "    distance = pd.Series()\n",
    "    for i in range(0,len(data)):\n",
    "        Xa = np.array(data.loc[i])\n",
    "        Xb = model.cluster_centers_[model.labels_[i]]\n",
    "        distance.set_value(i, np.linalg.norm(Xa-Xb))\n",
    "    return distance\n",
    "\n",
    "#设置异常值比例\n",
    "outliers_fraction = 0.01\n",
    "\n",
    "# 得到每个点到取聚类中心的距离，我们设置了10个聚类中心，kmeans[9]表示有10个聚类中心的模型\n",
    "distance = getDistanceByPoint(data, kmeans[9])\n",
    "\n",
    "#根据异常值比例outliers_fraction计算异常值的数量\n",
    "number_of_outliers = int(outliers_fraction*len(distance))\n",
    "\n",
    "#设定异常值的阈值\n",
    "threshold = distance.nlargest(number_of_outliers).min()\n",
    "\n",
    "#根据阈值来判断是否为异常值\n",
    "df['anomaly1'] = (distance >= threshold).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "colors = {0:'blue', 1:'red'}\n",
    "ax.scatter(df['principal_feature1'], df['principal_feature2'], c=df[\"anomaly1\"].apply(lambda x: colors[x]))\n",
    "plt.xlabel('principal feature1')\n",
    "plt.ylabel('principal feature2')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图中红色的点即是被认定的异常值，它们大约占总数据量的1%。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3018\n",
       "1      30\n",
       "Name: anomaly1, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.anomaly1.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>price_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012-11-01 14:20:52</td>\n",
       "      <td>211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2012-11-08 13:11:07</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2012-11-08 23:23:16</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>2012-11-18 16:01:13</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>2012-12-07 08:32:50</td>\n",
       "      <td>126.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              date_time  price_usd\n",
       "8   2012-11-01 14:20:52      211.0\n",
       "96  2012-11-08 13:11:07      230.0\n",
       "102 2012-11-08 23:23:16      225.0\n",
       "203 2012-11-18 16:01:13      116.0\n",
       "416 2012-12-07 08:32:50      126.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values('date_time')\n",
    "a = df.loc[df['anomaly1'] == 1, ['date_time', 'price_usd']] #anomaly\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.sort_values('date_time')\n",
    "df['date_time_int'] = df.date_time.astype(np.int64)\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "\n",
    "a = df.loc[df['anomaly1'] == 1, ['date_time_int', 'price_usd']] #anomaly\n",
    "\n",
    "ax.plot(df['date_time_int'], df['price_usd'], color='blue', label='正常值')\n",
    "ax.scatter(a['date_time_int'],a['price_usd'], color='red', label='异常值')\n",
    "plt.xlabel('Date Time Integer')\n",
    "plt.ylabel('价格(美元)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可知,经过PCA和KMeans计算出的异常值，它们的价格大多位于价格区间的最高点和最低点处，这应该是合理的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df.loc[df['anomaly1'] == 0, 'price_usd']\n",
    "b = df.loc[df['anomaly1'] == 1, 'price_usd']\n",
    "\n",
    "fig, axs = plt.subplots(figsize=(10,6))\n",
    "axs.hist([a,b], bins=32, stacked=True, color=['blue', 'red'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 孤立森林(IsolationForest)异常检测\n",
    "IsolationForest算法它是一种集成算法(类似于随机森林)主要用于挖掘异常(Anomaly)数据，或者说离群点挖掘，总之是在一大堆数据中，找出与其它数据的规律不太符合的数据。该算法不采样任何基于聚类或距离的方法，因此他和那些基于距离的的异常值检测算法有着根本上的不同，孤立森林认定异常值的原则是异常值是少数的和不同的数据。它通常用于网络安全中的攻击检测和流量异常等分析，金融机构则用于挖掘出欺诈行为。\n",
    "\n",
    "* 当我们使用IsolationForest算法时需要设置一个异常值比例的参数contamination， 该参数的作用类似于之前的outliers_fraction。\n",
    "* 使用 fit 方法对孤立森林模型进行训练\n",
    "* 使用 predict 方法去发现数据中的异常值。返回1表示正常值，-1表示异常值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:462: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\iforest.py:223: FutureWarning: behaviour=\"old\" is deprecated and will be removed in version 0.22. Please use behaviour=\"new\", which makes the decision_function change to match other anomaly detection algorithm API.\n",
      "  FutureWarning)\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\iforest.py:417: DeprecationWarning: threshold_ attribute is deprecated in 0.20 and will be removed in 0.22.\n",
      "  \" be removed in 0.22.\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[['price_usd', 'srch_booking_window', 'srch_saturday_night_bool']]\n",
    "scaler = StandardScaler()\n",
    "np_scaled = scaler.fit_transform(data)\n",
    "data = pd.DataFrame(np_scaled)\n",
    "# 训练孤立森林模型\n",
    "model =  IsolationForest(contamination=outliers_fraction)\n",
    "model.fit(data)\n",
    "\n",
    "#返回1表示正常值，-1表示异常值\n",
    "df['anomaly2'] = pd.Series(model.predict(data)) \n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "a = df.loc[df['anomaly2'] == -1, ['date_time_int', 'price_usd']] #异常值\n",
    "ax.plot(df['date_time_int'], df['price_usd'], color='blue', label = '正常值')\n",
    "ax.scatter(a['date_time_int'],a['price_usd'], color='red', label = '异常值')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可知，使用孤立森林预测的异常值，它们的价格大多位于价格区间的最高点或最低点处。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, -1], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['anomaly2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualisation of anomaly with avg price repartition\n",
    "a = df.loc[df['anomaly2'] == 1, 'price_usd']\n",
    "b = df.loc[df['anomaly2'] == -1, 'price_usd']\n",
    "\n",
    "fig, axs = plt.subplots(figsize=(10,6))\n",
    "axs.hist([a,b], bins=32, stacked=True, color=['blue', 'red'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 支持向量机(SVM)的异常检测\n",
    "SVM通常应用于监督式学习，但OneClassSVM算法可用于将异常检测这样的无监督式学习，它学习一个用于异常检测的决策函数其主要功能将新数据分类为与训练集相似的正常值或不相似的异常值。\n",
    "\n",
    "## OneClassSVM\n",
    "OneClassSVM的思想来源于这篇<a href=\"http://users.cecs.anu.edu.au/~williams/papers/P126.pdf\">论文</a>,SVM使用大边距的方法，它用于异常检测的主要思想是:将数据密度较高的区域分类为正，将数据密度较低的区域分类为负"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:462: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[['price_usd', 'srch_booking_window', 'srch_saturday_night_bool']]\n",
    "scaler = StandardScaler()\n",
    "np_scaled = scaler.fit_transform(data)\n",
    "data = pd.DataFrame(np_scaled)\n",
    "# 训练 oneclassSVM 模型\n",
    "model = OneClassSVM(nu=outliers_fraction, kernel=\"rbf\", gamma=0.01)\n",
    "model.fit(data)\n",
    " \n",
    "df['anomaly3'] = pd.Series(model.predict(data))\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "a = df.loc[df['anomaly3'] == -1, ['date_time_int', 'price_usd']] #anomaly\n",
    "\n",
    "ax.plot(df['date_time_int'], df['price_usd'], color='blue', label ='正常值')\n",
    "ax.scatter(a['date_time_int'],a['price_usd'], color='red', label = '异常值')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可知，使用OneClassSVM预测的异常值，它们的价格大多位于价格区间的最高点或最低点处。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df.loc[df['anomaly3'] == 1, 'price_usd']\n",
    "b = df.loc[df['anomaly3'] == -1, 'price_usd']\n",
    "\n",
    "fig, axs = plt.subplots(figsize=(10,6))\n",
    "axs.hist([a,b], bins=32, stacked=True, color=['blue', 'red'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 基于高斯概分布的异常检测\n",
    "\n",
    "高斯分布也称为正态分布。 它可以被用来进行异常值检测，不过我们首先要假设我们的数据是正态分布的。 不过这个假设不能适应于所有数据集。但如果我们做了这种假设那么它将会有一种有效的方法来发现异常值。\n",
    "\n",
    "Scikit-Learn的EllipticEnvelope模型，它在假设我们的数据是多元高斯分布的基础上计算出高斯分布的一些关键参数过程。过程大致如下:\n",
    "\n",
    "根据前面定义的类别创建两个不同的数据集 ： search_Sat_night和Search_Non_Sat_night。\n",
    "在每个类别应用EllipticEnvelope（高斯分布）。\n",
    "我们设置contamination参数，它表示我们数据集中异常值的比例。\n",
    "使用decision_function来计算给定数据的决策函数。 它等于移位的马氏距离(Mahalanobis distances)。 异常值的阈值为0，这确保了与其他异常值检测算法的兼容性。\n",
    "使用predict 来预测数据是否为异常值(1 正常值, -1 异常值)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_class0 = df.loc[df['srch_saturday_night_bool'] == 0, 'price_usd']\n",
    "df_class1 = df.loc[df['srch_saturday_night_bool'] == 1, 'price_usd']\n",
    "\n",
    "fig, axs = plt.subplots(1,2)\n",
    "df_class0.hist(ax=axs[0], bins=30)\n",
    "df_class1.hist(ax=axs[1], bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "envelope =  EllipticEnvelope(contamination = outliers_fraction) \n",
    "X_train = df_class0.values.reshape(-1,1)\n",
    "envelope.fit(X_train)\n",
    "df_class0 = pd.DataFrame(df_class0)\n",
    "df_class0['deviation'] = envelope.decision_function(X_train)\n",
    "df_class0['anomaly'] = envelope.predict(X_train)\n",
    "\n",
    "envelope =  EllipticEnvelope(contamination = outliers_fraction) \n",
    "X_train = df_class1.values.reshape(-1,1)\n",
    "envelope.fit(X_train)\n",
    "df_class1 = pd.DataFrame(df_class1)\n",
    "df_class1['deviation'] = envelope.decision_function(X_train)\n",
    "df_class1['anomaly'] = envelope.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the price repartition by categories with anomalies\n",
    "a0 = df_class0.loc[df_class0['anomaly'] == 1, 'price_usd']\n",
    "b0 = df_class0.loc[df_class0['anomaly'] == -1, 'price_usd']\n",
    "\n",
    "a2 = df_class1.loc[df_class1['anomaly'] == 1, 'price_usd']\n",
    "b2 = df_class1.loc[df_class1['anomaly'] == -1, 'price_usd']\n",
    "\n",
    "fig, axs = plt.subplots(1,2)\n",
    "axs[0].hist([a0,b0], bins=32, stacked=True, color=['blue', 'red'])\n",
    "axs[1].hist([a2,b2], bins=32, stacked=True, color=['blue', 'red'])\n",
    "axs[0].set_title(\"Search Non Saturday Night\")\n",
    "axs[1].set_title(\"Search Saturday Night\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add the data to the main \n",
    "df_class = pd.concat([df_class0, df_class1])\n",
    "df['anomaly5'] = df_class['anomaly']\n",
    "# df['anomaly5'] = np.array(df['anomaly22'] == -1).astype(int)\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "a = df.loc[df['anomaly5'] == -1, ('date_time_int', 'price_usd')] #anomaly\n",
    "ax.plot(df['date_time_int'], df['price_usd'], color='blue', label='Normal')\n",
    "ax.scatter(a['date_time_int'],a['price_usd'], color='red', label='Anomaly')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可知，使用EllipticEnvelope预测的异常值，它们的价格大多位于价格区间的最高点处在最低点处没有出现异常值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df.loc[df['anomaly5'] == 1, 'price_usd']\n",
    "b = df.loc[df['anomaly5'] == -1, 'price_usd']\n",
    "\n",
    "fig, axs = plt.subplots(figsize=(10, 6))\n",
    "axs.hist([a,b], bins=32, stacked=True, color=['blue', 'red'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "到目前为止，我们已经用四种不同的方法进行了价格异常检测。 因为我们的异常检测是无监督学习。 在构建模型之后，我们不知道他们的异常检测效果怎么样，因为我们没有办法可以对他们进行测试。 通常异常检测只有在实际的应用场景中才能测试出它的效果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 参考\n",
    "\n",
    "* <a href=\"https://www.datascience.com/blog/python-anomaly-detection\">Introduction to Anomaly Detection</a>\n",
    "* <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html\">sklearn.ensemble.IsolationForest</a>\n",
    "* <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html\">sklearn.svm.OneClassSVM</a>\n",
    "* <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html\">sklearn.covariance.EllipticEnvelope</a>\n",
    "* <a href=\"https://www.kaggle.com/victorambonati/unsupervised-anomaly-detection\">Unsupervised Anomaly Detection | kaggle</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
